Overview

Dataset statistics

Number of variables44
Number of observations20336
Missing cells222441
Missing cells (%)24.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Unsupported1
Categorical4

Alerts

SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
Unit1 is highly correlated with Unit2High correlation
Temp has 235 (1.2%) missing values Missing
SBP has 258 (1.3%) missing values Missing
DBP has 7384 (36.3%) missing values Missing
EtCO2 has 20336 (100.0%) missing values Missing
BaseExcess has 7684 (37.8%) missing values Missing
HCO3 has 535 (2.6%) missing values Missing
FiO2 has 8349 (41.1%) missing values Missing
pH has 7155 (35.2%) missing values Missing
PaCO2 has 7759 (38.2%) missing values Missing
SaO2 has 12373 (60.8%) missing values Missing
AST has 14443 (71.0%) missing values Missing
BUN has 427 (2.1%) missing values Missing
Alkalinephos has 14633 (72.0%) missing values Missing
Calcium has 3789 (18.6%) missing values Missing
Chloride has 542 (2.7%) missing values Missing
Creatinine has 461 (2.3%) missing values Missing
Bilirubin_direct has 19750 (97.1%) missing values Missing
Glucose has 407 (2.0%) missing values Missing
Lactate has 12603 (62.0%) missing values Missing
Magnesium has 1388 (6.8%) missing values Missing
Phosphate has 3650 (17.9%) missing values Missing
Potassium has 433 (2.1%) missing values Missing
Bilirubin_total has 14566 (71.6%) missing values Missing
TroponinI has 19847 (97.6%) missing values Missing
Hct has 364 (1.8%) missing values Missing
Hgb has 507 (2.5%) missing values Missing
PTT has 4496 (22.1%) missing values Missing
WBC has 625 (3.1%) missing values Missing
Fibrinogen has 17769 (87.4%) missing values Missing
Platelets has 585 (2.9%) missing values Missing
Unit1 has 9522 (46.8%) missing values Missing
Unit2 has 9522 (46.8%) missing values Missing
PatientID has unique values Unique
EtCO2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
BaseExcess has 1018 (5.0%) zeros Zeros
SepsisLabel has 18546 (91.2%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:21:21.381538
Analysis finished2021-11-29 10:21:35.408062
Duration14.03 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIQUE

Distinct20336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10173.60651
Minimum1
Maximum20643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:35.458071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1017.75
Q15084.75
median10168.5
Q315252.25
95-th percentile19320.25
Maximum20643
Range20642
Interquartile range (IQR)10167.5

Descriptive statistics

Standard deviation5879.461518
Coefficient of variation (CV)0.5779132024
Kurtosis-1.192915145
Mean10173.60651
Median Absolute Deviation (MAD)5084
Skewness0.005160825078
Sum206890462
Variance34568067.75
MonotonicityStrictly increasing
2021-11-29T11:21:35.557663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
135561
 
< 0.1%
135631
 
< 0.1%
135621
 
< 0.1%
135611
 
< 0.1%
135601
 
< 0.1%
135591
 
< 0.1%
135581
 
< 0.1%
135571
 
< 0.1%
135551
 
< 0.1%
Other values (20326)20326
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
206431
< 0.1%
206421
< 0.1%
206411
< 0.1%
206401
< 0.1%
206391
< 0.1%
206381
< 0.1%
206371
< 0.1%
206361
< 0.1%
206351
< 0.1%
206341
< 0.1%

HR
Real number (ℝ≥0)

Distinct15835
Distinct (%)77.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean84.54890909
Minimum30.25806452
Maximum150.8888889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:35.660262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.25806452
5-th percentile62.23484848
Q174.67936623
median83.83333333
Q393.63794926
95-th percentile108.950766
Maximum150.8888889
Range120.6308244
Interquartile range (IQR)18.95858303

Descriptive statistics

Standard deviation14.22214204
Coefficient of variation (CV)0.1682120111
Kurtosis0.1909661708
Mean84.54890909
Median Absolute Deviation (MAD)9.49122807
Skewness0.2977791885
Sum1719302.066
Variance202.2693241
MonotonicityNot monotonic
2021-11-29T11:21:35.765534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8821
 
0.1%
8018
 
0.1%
7017
 
0.1%
8117
 
0.1%
8316
 
0.1%
78.516
 
0.1%
7715
 
0.1%
7815
 
0.1%
7414
 
0.1%
9014
 
0.1%
Other values (15825)20172
99.2%
ValueCountFrequency (%)
30.258064521
< 0.1%
35.410256411
< 0.1%
39.619047621
< 0.1%
40.561403511
< 0.1%
411
< 0.1%
42.014285711
< 0.1%
42.411764711
< 0.1%
42.588235291
< 0.1%
42.765957451
< 0.1%
43.583333331
< 0.1%
ValueCountFrequency (%)
150.88888891
< 0.1%
150.16666671
< 0.1%
145.81
< 0.1%
145.25862071
< 0.1%
145.15384621
< 0.1%
144.18292681
< 0.1%
143.78260871
< 0.1%
142.43751
< 0.1%
142.06060611
< 0.1%
141.35294121
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct7624
Distinct (%)37.5%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean97.20110086
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:35.868240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile94.29685185
Q196.27272727
median97.44680851
Q398.51681034
95-th percentile99.65
Maximum100
Range73
Interquartile range (IQR)2.244083072

Descriptive statistics

Standard deviation2.233046467
Coefficient of variation (CV)0.02297346889
Kurtosis138.6936321
Mean97.20110086
Median Absolute Deviation (MAD)1.115691489
Skewness-7.318368528
Sum1975515.174
Variance4.986496523
MonotonicityNot monotonic
2021-11-29T11:21:35.968122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100166
 
0.8%
97132
 
0.6%
99102
 
0.5%
98101
 
0.5%
9699
 
0.5%
97.587
 
0.4%
98.585
 
0.4%
96.573
 
0.4%
99.560
 
0.3%
9553
 
0.3%
Other values (7614)19366
95.2%
ValueCountFrequency (%)
271
< 0.1%
34.251
< 0.1%
491
< 0.1%
55.51
< 0.1%
56.302631581
< 0.1%
57.51
< 0.1%
61.954545451
< 0.1%
65.81
< 0.1%
66.251
< 0.1%
66.51
< 0.1%
ValueCountFrequency (%)
100166
0.8%
99.988888891
 
< 0.1%
99.986842111
 
< 0.1%
99.986111111
 
< 0.1%
99.983870972
 
< 0.1%
99.983333331
 
< 0.1%
99.982758621
 
< 0.1%
99.981818181
 
< 0.1%
99.982
 
< 0.1%
99.979591841
 
< 0.1%

Temp
Real number (ℝ≥0)

MISSING

Distinct10695
Distinct (%)53.2%
Missing235
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.91389324
Minimum30.5
Maximum39.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:36.074679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile36.06636364
Q136.561
median36.90285714
Q337.26333333
95-th percentile37.81818182
Maximum39.61
Range9.11
Interquartile range (IQR)0.7023333333

Descriptive statistics

Standard deviation0.5575562462
Coefficient of variation (CV)0.01510423847
Kurtosis3.197251831
Mean36.91389324
Median Absolute Deviation (MAD)0.3500571429
Skewness-0.2923179345
Sum742006.1681
Variance0.3108689677
MonotonicityNot monotonic
2021-11-29T11:21:36.172232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.554
 
0.3%
3754
 
0.3%
36.8954
 
0.3%
36.6148
 
0.2%
36.3946
 
0.2%
36.7537
 
0.2%
36.8333333330
 
0.1%
36.7229
 
0.1%
36.94529
 
0.1%
36.7828
 
0.1%
Other values (10685)19692
96.8%
(Missing)235
 
1.2%
ValueCountFrequency (%)
30.51
< 0.1%
32.351
< 0.1%
32.456363641
< 0.1%
32.521
< 0.1%
32.751
< 0.1%
33.041
< 0.1%
33.068421051
< 0.1%
33.116666671
< 0.1%
33.171
< 0.1%
33.38251
< 0.1%
ValueCountFrequency (%)
39.611
< 0.1%
39.3851
< 0.1%
39.3581
< 0.1%
39.34751
< 0.1%
39.2031
< 0.1%
39.1561
< 0.1%
39.1541
< 0.1%
39.110909091
< 0.1%
39.111
< 0.1%
39.091428571
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15880
Distinct (%)79.1%
Missing258
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean120.24809
Minimum35
Maximum214.8181818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:36.271038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile97.56234631
Q1108.5416667
median118.0976935
Q3130.2128258
95-th percentile150.3529419
Maximum214.8181818
Range179.8181818
Interquartile range (IQR)21.67115909

Descriptive statistics

Standard deviation16.44958943
Coefficient of variation (CV)0.1367970953
Kurtosis0.6297223171
Mean120.24809
Median Absolute Deviation (MAD)10.55658993
Skewness0.5637393354
Sum2414341.152
Variance270.5889925
MonotonicityNot monotonic
2021-11-29T11:21:36.371339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.519
 
0.1%
11718
 
0.1%
115.518
 
0.1%
118.517
 
0.1%
12517
 
0.1%
11616
 
0.1%
11316
 
0.1%
107.515
 
0.1%
10815
 
0.1%
11915
 
0.1%
Other values (15870)19912
97.9%
(Missing)258
 
1.3%
ValueCountFrequency (%)
351
< 0.1%
48.517241381
< 0.1%
52.2751
< 0.1%
57.31251
< 0.1%
57.392857141
< 0.1%
58.166666671
< 0.1%
58.7251
< 0.1%
63.024390241
< 0.1%
63.051
< 0.1%
63.159090911
< 0.1%
ValueCountFrequency (%)
214.81818181
< 0.1%
207.05555561
< 0.1%
197.79166671
< 0.1%
194.66666671
< 0.1%
193.36363641
< 0.1%
191.86842111
< 0.1%
188.2450981
< 0.1%
187.751
< 0.1%
187.64705881
< 0.1%
1871
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17616
Distinct (%)86.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean78.47385076
Minimum22
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:36.475517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile63.52564938
Q170.95047619
median77.05555556
Q384.76273148
95-th percentile97.9781875
Maximum149
Range127
Interquartile range (IQR)13.81225529

Descriptive statistics

Standard deviation10.78266445
Coefficient of variation (CV)0.1374045538
Kurtosis1.015771754
Mean78.47385076
Median Absolute Deviation (MAD)6.766929825
Skewness0.6675458805
Sum1595687.281
Variance116.2658527
MonotonicityNot monotonic
2021-11-29T11:21:36.582238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7917
 
0.1%
7317
 
0.1%
7116
 
0.1%
79.515
 
0.1%
73.6666666714
 
0.1%
7413
 
0.1%
7813
 
0.1%
7613
 
0.1%
6912
 
0.1%
7512
 
0.1%
Other values (17606)20192
99.3%
ValueCountFrequency (%)
221
< 0.1%
32.425185191
< 0.1%
36.19151
< 0.1%
40.634193551
< 0.1%
411
< 0.1%
41.81
< 0.1%
41.9171
< 0.1%
43.0421
< 0.1%
43.2491
< 0.1%
43.51
< 0.1%
ValueCountFrequency (%)
1491
< 0.1%
141.54166671
< 0.1%
140.06578951
< 0.1%
133.14814811
< 0.1%
131.751
< 0.1%
131.28681
< 0.1%
131.11166671
< 0.1%
130.093751
< 0.1%
129.81251
< 0.1%
129.36363641
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9586
Distinct (%)74.0%
Missing7384
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean60.1749596
Minimum27.33333333
Maximum135.7222222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:36.778605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27.33333333
5-th percentile46.05235369
Q153.34050325
median59.01934524
Q365.85958501
95-th percentile78.06965409
Maximum135.7222222
Range108.3888889
Interquartile range (IQR)12.51908177

Descriptive statistics

Standard deviation9.920291131
Coefficient of variation (CV)0.1648574623
Kurtosis1.447456563
Mean60.1749596
Median Absolute Deviation (MAD)6.153132992
Skewness0.7141169379
Sum779386.0767
Variance98.41217612
MonotonicityNot monotonic
2021-11-29T11:21:36.879663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5526
 
0.1%
5922
 
0.1%
6121
 
0.1%
6221
 
0.1%
6020
 
0.1%
6820
 
0.1%
5419
 
0.1%
6319
 
0.1%
52.519
 
0.1%
5618
 
0.1%
Other values (9576)12747
62.7%
(Missing)7384
36.3%
ValueCountFrequency (%)
27.333333331
 
< 0.1%
27.5251
 
< 0.1%
28.583333331
 
< 0.1%
293
< 0.1%
29.095744681
 
< 0.1%
30.764705881
 
< 0.1%
32.4751
 
< 0.1%
32.666666671
 
< 0.1%
32.833333331
 
< 0.1%
32.913636361
 
< 0.1%
ValueCountFrequency (%)
135.72222221
< 0.1%
1341
< 0.1%
116.54545451
< 0.1%
115.83333331
< 0.1%
110.751
< 0.1%
109.91
< 0.1%
108.6251
< 0.1%
1081
< 0.1%
107.81
< 0.1%
107.19565221
< 0.1%

Resp
Real number (ℝ≥0)

Distinct11537
Distinct (%)56.8%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean18.59127165
Minimum7.739130435
Maximum41.66666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:36.980813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.739130435
5-th percentile13.5625
Q116.05952381
median18.09175084
Q320.57366558
95-th percentile25.32476351
Maximum41.66666667
Range33.92753623
Interquartile range (IQR)4.514141768

Descriptive statistics

Standard deviation3.651738089
Coefficient of variation (CV)0.1964221791
Kurtosis1.590410436
Mean18.59127165
Median Absolute Deviation (MAD)2.216750842
Skewness0.903422218
Sum377551.5446
Variance13.33519107
MonotonicityNot monotonic
2021-11-29T11:21:37.079197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1671
 
0.3%
1862
 
0.3%
1758
 
0.3%
2049
 
0.2%
17.548
 
0.2%
16.545
 
0.2%
1944
 
0.2%
1543
 
0.2%
18.541
 
0.2%
15.540
 
0.2%
Other values (11527)19807
97.4%
ValueCountFrequency (%)
7.7391304351
< 0.1%
8.4047619051
< 0.1%
91
< 0.1%
9.21
< 0.1%
9.3636363641
< 0.1%
9.4293478261
< 0.1%
9.51
< 0.1%
9.6086956521
< 0.1%
9.6666666671
< 0.1%
9.7908163271
< 0.1%
ValueCountFrequency (%)
41.666666671
< 0.1%
40.222222221
< 0.1%
39.51
< 0.1%
38.153846151
< 0.1%
37.913043481
< 0.1%
37.751
< 0.1%
37.357142861
< 0.1%
37.31251
< 0.1%
36.877272731
< 0.1%
36.751
< 0.1%

EtCO2
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing20336
Missing (%)100.0%
Memory size159.0 KiB

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1807
Distinct (%)14.3%
Missing7684
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean-0.3804393698
Minimum-25
Maximum25
Zeros1018
Zeros (%)5.0%
Negative6636
Negative (%)32.6%
Memory size159.0 KiB
2021-11-29T11:21:37.182095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile-6.153571429
Q1-2
median-0.2857142857
Q31.2
95-th percentile5.75
Maximum25
Range50
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.825150717
Coefficient of variation (CV)-10.05456065
Kurtosis4.943321824
Mean-0.3804393698
Median Absolute Deviation (MAD)1.714285714
Skewness-0.0485160246
Sum-4813.318907
Variance14.63177801
MonotonicityNot monotonic
2021-11-29T11:21:37.277656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01018
 
5.0%
-1486
 
2.4%
1463
 
2.3%
-2369
 
1.8%
2321
 
1.6%
3251
 
1.2%
-3239
 
1.2%
-0.5216
 
1.1%
0.5212
 
1.0%
4175
 
0.9%
Other values (1797)8902
43.8%
(Missing)7684
37.8%
ValueCountFrequency (%)
-251
< 0.1%
-24.51
< 0.1%
-242
< 0.1%
-22.666666671
< 0.1%
-22.251
< 0.1%
-21.751
< 0.1%
-21.61
< 0.1%
-21.071428571
< 0.1%
-20.52
< 0.1%
-20.333333331
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
241
 
< 0.1%
212
< 0.1%
20.714285711
 
< 0.1%
201
 
< 0.1%
19.333333331
 
< 0.1%
193
< 0.1%
18.818181821
 
< 0.1%
18.81
 
< 0.1%
183
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct791
Distinct (%)4.0%
Missing535
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean24.40642358
Minimum5
Maximum54.33333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:37.371317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile18
Q122.2
median24.33333333
Q326.5
95-th percentile30.5
Maximum54.33333333
Range49.33333333
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.90020506
Coefficient of variation (CV)0.1598023998
Kurtosis3.089016315
Mean24.40642358
Median Absolute Deviation (MAD)2.166666667
Skewness0.4065426165
Sum483271.5934
Variance15.21159951
MonotonicityNot monotonic
2021-11-29T11:21:37.470301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251367
 
6.7%
241340
 
6.6%
261163
 
5.7%
231153
 
5.7%
22891
 
4.4%
27873
 
4.3%
28576
 
2.8%
21565
 
2.8%
24.5533
 
2.6%
23.5460
 
2.3%
Other values (781)10880
53.5%
(Missing)535
 
2.6%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
6.52
 
< 0.1%
71
 
< 0.1%
7.21
 
< 0.1%
7.4285714291
 
< 0.1%
85
< 0.1%
8.3636363641
 
< 0.1%
8.51
 
< 0.1%
95
< 0.1%
ValueCountFrequency (%)
54.333333331
< 0.1%
511
< 0.1%
501
< 0.1%
481
< 0.1%
47.666666672
< 0.1%
46.51
< 0.1%
46.333333331
< 0.1%
462
< 0.1%
45.51
< 0.1%
45.251
< 0.1%

FiO2
Real number (ℝ≥0)

MISSING

Distinct1891
Distinct (%)15.8%
Missing8349
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean0.5401693434
Minimum0
Maximum10
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:37.571190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3766666667
Q10.4428571429
median0.5055555556
Q30.6
95-th percentile0.8333333333
Maximum10
Range10
Interquartile range (IQR)0.1571428571

Descriptive statistics

Standard deviation0.1667768776
Coefficient of variation (CV)0.3087492462
Kurtosis863.7380682
Mean0.5401693434
Median Absolute Deviation (MAD)0.07777777778
Skewness15.93688734
Sum6475.009919
Variance0.0278145269
MonotonicityNot monotonic
2021-11-29T11:21:37.674731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51114
 
5.5%
0.4688
 
3.4%
0.6338
 
1.7%
1312
 
1.5%
0.45231
 
1.1%
0.55195
 
1.0%
0.625192
 
0.9%
0.6666666667166
 
0.8%
0.4164
 
0.8%
0.7137
 
0.7%
Other values (1881)8450
41.6%
(Missing)8349
41.1%
ValueCountFrequency (%)
02
< 0.1%
0.022
< 0.1%
0.034
< 0.1%
0.043
< 0.1%
0.081
 
< 0.1%
0.111
 
< 0.1%
0.151
 
< 0.1%
0.181
 
< 0.1%
0.1841
 
< 0.1%
0.21
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
1312
1.5%
0.99666666671
 
< 0.1%
0.99473684211
 
< 0.1%
0.99333333331
 
< 0.1%
0.99285714291
 
< 0.1%
0.99166666671
 
< 0.1%
0.994
 
< 0.1%
0.98842105261
 
< 0.1%
0.98751
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2188
Distinct (%)16.6%
Missing7155
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean7.383642509
Minimum6.63
Maximum7.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:37.779457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.63
5-th percentile7.29
Q17.355
median7.386666667
Q37.42
95-th percentile7.47
Maximum7.73
Range1.1
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.05900372908
Coefficient of variation (CV)0.007991141095
Kurtosis9.115096707
Mean7.383642509
Median Absolute Deviation (MAD)0.03238095238
Skewness-1.361189307
Sum97323.79191
Variance0.003481440046
MonotonicityNot monotonic
2021-11-29T11:21:37.875737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4329
 
1.6%
7.37328
 
1.6%
7.38324
 
1.6%
7.42259
 
1.3%
7.43244
 
1.2%
7.41230
 
1.1%
7.39188
 
0.9%
7.44184
 
0.9%
7.35177
 
0.9%
7.36170
 
0.8%
Other values (2178)10748
52.9%
(Missing)7155
35.2%
ValueCountFrequency (%)
6.631
< 0.1%
6.651
< 0.1%
6.871
< 0.1%
6.91
< 0.1%
6.941
< 0.1%
6.941
< 0.1%
6.951
< 0.1%
6.961
< 0.1%
6.9621
< 0.1%
6.9751
< 0.1%
ValueCountFrequency (%)
7.731
< 0.1%
7.6951
< 0.1%
7.661
< 0.1%
7.631
< 0.1%
7.591
< 0.1%
7.5851
< 0.1%
7.5751
< 0.1%
7.5733333331
< 0.1%
7.5666666671
< 0.1%
7.5651
< 0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2061
Distinct (%)16.4%
Missing7759
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean41.14150912
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:37.976747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile31
Q136.875
median40.26923077
Q344.1875
95-th percentile53.40571429
Maximum98
Range88
Interquartile range (IQR)7.3125

Descriptive statistics

Standard deviation7.683341373
Coefficient of variation (CV)0.1867539995
Kurtosis7.048028623
Mean41.14150912
Median Absolute Deviation (MAD)3.730769231
Skewness1.704228689
Sum517436.7602
Variance59.03373465
MonotonicityNot monotonic
2021-11-29T11:21:38.074393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40303
 
1.5%
38283
 
1.4%
41280
 
1.4%
39278
 
1.4%
43262
 
1.3%
42260
 
1.3%
37253
 
1.2%
44238
 
1.2%
36202
 
1.0%
45195
 
1.0%
Other values (2051)10023
49.3%
(Missing)7759
38.2%
ValueCountFrequency (%)
101
< 0.1%
15.51
< 0.1%
162
< 0.1%
171
< 0.1%
18.751
< 0.1%
18.833333331
< 0.1%
191
< 0.1%
19.142857141
< 0.1%
19.21
< 0.1%
19.251
< 0.1%
ValueCountFrequency (%)
981
< 0.1%
96.751
< 0.1%
95.333333331
< 0.1%
941
< 0.1%
93.51
< 0.1%
932
< 0.1%
91.666666671
< 0.1%
91.251
< 0.1%
901
< 0.1%
89.51
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct1382
Distinct (%)17.4%
Missing12373
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean91.7306866
Minimum29.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:38.251298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile71
Q189.29705882
median96
Q397.7
95-th percentile98.66666667
Maximum100
Range70.5
Interquartile range (IQR)8.402941176

Descriptive statistics

Standard deviation9.105877385
Coefficient of variation (CV)0.09926751584
Kurtosis4.497235356
Mean91.7306866
Median Absolute Deviation (MAD)2
Skewness-2.023994356
Sum730451.4574
Variance82.91700296
MonotonicityNot monotonic
2021-11-29T11:21:38.348049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
981025
 
5.0%
97576
 
2.8%
99315
 
1.5%
96309
 
1.5%
97.5226
 
1.1%
98.5170
 
0.8%
95160
 
0.8%
96.5113
 
0.6%
94110
 
0.5%
97.6666666798
 
0.5%
Other values (1372)4861
 
23.9%
(Missing)12373
60.8%
ValueCountFrequency (%)
29.51
< 0.1%
301
< 0.1%
31.51
< 0.1%
32.251
< 0.1%
401
< 0.1%
41.251
< 0.1%
41.333333331
< 0.1%
41.51
< 0.1%
421
< 0.1%
431
< 0.1%
ValueCountFrequency (%)
10018
 
0.1%
99.52
 
< 0.1%
99.333333331
 
< 0.1%
99.22
 
< 0.1%
99315
1.5%
98.833333333
 
< 0.1%
98.821428571
 
< 0.1%
98.81
 
< 0.1%
98.791666671
 
< 0.1%
98.7513
 
0.1%

AST
Real number (ℝ≥0)

MISSING

Distinct1304
Distinct (%)22.1%
Missing14443
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean185.7381345
Minimum3
Maximum9210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:38.452610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14
Q123.33333333
median43
Q398
95-th percentile688.6
Maximum9210
Range9207
Interquartile range (IQR)74.66666667

Descriptive statistics

Standard deviation618.0870944
Coefficient of variation (CV)3.327733941
Kurtosis71.7154808
Mean185.7381345
Median Absolute Deviation (MAD)24
Skewness7.728193141
Sum1094554.827
Variance382031.6562
MonotonicityNot monotonic
2021-11-29T11:21:38.549801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19126
 
0.6%
18125
 
0.6%
24119
 
0.6%
17119
 
0.6%
16116
 
0.6%
22113
 
0.6%
20106
 
0.5%
15104
 
0.5%
21103
 
0.5%
2395
 
0.5%
Other values (1294)4767
 
23.4%
(Missing)14443
71.0%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
65
 
< 0.1%
75
 
< 0.1%
810
< 0.1%
8.251
 
< 0.1%
8.41
 
< 0.1%
8.53
 
< 0.1%
914
0.1%
ValueCountFrequency (%)
92101
< 0.1%
85911
< 0.1%
7868.81
< 0.1%
7697.3333331
< 0.1%
7438.51
< 0.1%
73931
< 0.1%
7279.41
< 0.1%
71741
< 0.1%
6982.51
< 0.1%
6910.51
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1554
Distinct (%)7.8%
Missing427
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean22.57039553
Minimum1.285714286
Maximum227.3333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:38.649916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.285714286
5-th percentile7
Q112
median17
Q326
95-th percentile59.27142857
Maximum227.3333333
Range226.047619
Interquartile range (IQR)14

Descriptive statistics

Standard deviation18.18592295
Coefficient of variation (CV)0.8057423239
Kurtosis10.13880362
Mean22.57039553
Median Absolute Deviation (MAD)6
Skewness2.679689373
Sum449354.0046
Variance330.7277934
MonotonicityNot monotonic
2021-11-29T11:21:38.743788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11578
 
2.8%
14571
 
2.8%
13566
 
2.8%
12541
 
2.7%
15508
 
2.5%
10486
 
2.4%
16475
 
2.3%
17442
 
2.2%
9435
 
2.1%
18389
 
1.9%
Other values (1544)14918
73.4%
(Missing)427
 
2.1%
ValueCountFrequency (%)
1.2857142861
 
< 0.1%
1.51
 
< 0.1%
1.6666666671
 
< 0.1%
1.8571428571
 
< 0.1%
28
 
< 0.1%
2.3333333332
 
< 0.1%
2.55
 
< 0.1%
2.6666666673
 
< 0.1%
2.82
 
< 0.1%
334
0.2%
ValueCountFrequency (%)
227.33333331
< 0.1%
187.51
< 0.1%
178.51
< 0.1%
1771
< 0.1%
170.51
< 0.1%
169.51
< 0.1%
1681
< 0.1%
166.751
< 0.1%
163.33333331
< 0.1%
159.33333331
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct1107
Distinct (%)19.4%
Missing14633
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean109.4410878
Minimum7
Maximum3726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:38.841008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile37
Q157
median77
Q3114
95-th percentile278
Maximum3726
Range3719
Interquartile range (IQR)57

Descriptive statistics

Standard deviation131.1173409
Coefficient of variation (CV)1.19806321
Kurtosis169.7134387
Mean109.4410878
Median Absolute Deviation (MAD)25
Skewness9.646002046
Sum624142.5237
Variance17191.75709
MonotonicityNot monotonic
2021-11-29T11:21:38.939230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5569
 
0.3%
6367
 
0.3%
6767
 
0.3%
5967
 
0.3%
6065
 
0.3%
4964
 
0.3%
6462
 
0.3%
6162
 
0.3%
5361
 
0.3%
6660
 
0.3%
Other values (1097)5059
 
24.9%
(Missing)14633
72.0%
ValueCountFrequency (%)
71
< 0.1%
121
< 0.1%
151
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
19.52
< 0.1%
202
< 0.1%
222
< 0.1%
22.333333331
< 0.1%
ValueCountFrequency (%)
37261
< 0.1%
25281
< 0.1%
2182.6666671
< 0.1%
2145.51
< 0.1%
20201
< 0.1%
16691
< 0.1%
15291
< 0.1%
1477.6666671
< 0.1%
14371
< 0.1%
12881
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct905
Distinct (%)5.5%
Missing3789
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean8.347046095
Minimum3.9
Maximum16.76666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:39.043572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile7.3
Q17.9
median8.325
Q38.75
95-th percentile9.433333333
Maximum16.76666667
Range12.86666667
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation0.6981063075
Coefficient of variation (CV)0.08363513267
Kurtosis6.594771934
Mean8.347046095
Median Absolute Deviation (MAD)0.425
Skewness0.7262989842
Sum138118.5717
Variance0.4873524166
MonotonicityNot monotonic
2021-11-29T11:21:39.145774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5656
 
3.2%
8.3603
 
3.0%
8.6597
 
2.9%
8.4595
 
2.9%
8.2567
 
2.8%
8.1550
 
2.7%
8508
 
2.5%
8.7471
 
2.3%
8.8463
 
2.3%
7.9395
 
1.9%
Other values (895)11142
54.8%
(Missing)3789
 
18.6%
ValueCountFrequency (%)
3.91
 
< 0.1%
4.51
 
< 0.1%
4.6714285711
 
< 0.1%
4.72
< 0.1%
5.33
< 0.1%
5.41
 
< 0.1%
5.63
< 0.1%
5.71
 
< 0.1%
5.72
< 0.1%
5.76251
 
< 0.1%
ValueCountFrequency (%)
16.766666671
< 0.1%
15.71
< 0.1%
15.651
< 0.1%
15.41
< 0.1%
14.951
< 0.1%
14.8251
< 0.1%
13.951
< 0.1%
13.61
< 0.1%
13.333333331
< 0.1%
13.21
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct930
Distinct (%)4.7%
Missing542
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean105.5217411
Minimum67.5
Maximum138.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:39.246705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum67.5
5-th percentile97
Q1102.6666667
median106
Q3108.6666667
95-th percentile113.3333333
Maximum138.6
Range71.1
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.122767032
Coefficient of variation (CV)0.04854702904
Kurtosis1.836872713
Mean105.5217411
Median Absolute Deviation (MAD)3
Skewness-0.2649815802
Sum2088697.344
Variance26.24274206
MonotonicityNot monotonic
2021-11-29T11:21:39.343966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107913
 
4.5%
106911
 
4.5%
105884
 
4.3%
108781
 
3.8%
103749
 
3.7%
104722
 
3.6%
109647
 
3.2%
102553
 
2.7%
110515
 
2.5%
101491
 
2.4%
Other values (920)12628
62.1%
(Missing)542
 
2.7%
ValueCountFrequency (%)
67.51
 
< 0.1%
731
 
< 0.1%
741
 
< 0.1%
75.555555561
 
< 0.1%
78.285714291
 
< 0.1%
79.666666671
 
< 0.1%
80.333333331
 
< 0.1%
813
< 0.1%
81.333333331
 
< 0.1%
821
 
< 0.1%
ValueCountFrequency (%)
138.61
< 0.1%
135.1251
< 0.1%
133.751
< 0.1%
1331
< 0.1%
132.83333331
< 0.1%
1321
< 0.1%
1312
< 0.1%
129.89473681
< 0.1%
1291
< 0.1%
128.52
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1326
Distinct (%)6.7%
Missing461
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1.298793482
Minimum0.1
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:39.445631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.7
median0.9
Q31.24
95-th percentile3.925
Maximum27.4
Range27.3
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation1.417181747
Coefficient of variation (CV)1.091152494
Kurtosis29.28253775
Mean1.298793482
Median Absolute Deviation (MAD)0.25
Skewness4.486914185
Sum25813.52045
Variance2.008404104
MonotonicityNot monotonic
2021-11-29T11:21:39.539053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71301
 
6.4%
0.81268
 
6.2%
0.91206
 
5.9%
0.61144
 
5.6%
1893
 
4.4%
0.5722
 
3.6%
0.75568
 
2.8%
1.1531
 
2.6%
0.85431
 
2.1%
0.65429
 
2.1%
Other values (1316)11382
56.0%
(Missing)461
 
2.3%
ValueCountFrequency (%)
0.13
 
< 0.1%
0.11
 
< 0.1%
0.13333333333
 
< 0.1%
0.153
 
< 0.1%
0.16666666671
 
< 0.1%
0.23
 
< 0.1%
0.211
0.1%
0.23
 
< 0.1%
0.23333333332
 
< 0.1%
0.2511
0.1%
ValueCountFrequency (%)
27.41
< 0.1%
20.966666671
< 0.1%
18.21
< 0.1%
17.61
< 0.1%
17.31
< 0.1%
15.851
< 0.1%
15.71
< 0.1%
15.61
< 0.1%
15.516666671
< 0.1%
15.2751
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct201
Distinct (%)34.3%
Missing19750
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean2.642075183
Minimum0.1
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:39.713566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median1
Q32.9
95-th percentile10.6125
Maximum37.5
Range37.4
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.413299266
Coefficient of variation (CV)1.670391249
Kurtosis18.3851269
Mean2.642075183
Median Absolute Deviation (MAD)0.8
Skewness3.767365242
Sum1548.256057
Variance19.47721041
MonotonicityNot monotonic
2021-11-29T11:21:39.809566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.252
 
0.3%
0.143
 
0.2%
0.438
 
0.2%
0.329
 
0.1%
0.624
 
0.1%
0.523
 
0.1%
0.816
 
0.1%
0.713
 
0.1%
1.113
 
0.1%
110
 
< 0.1%
Other values (191)325
 
1.6%
(Missing)19750
97.1%
ValueCountFrequency (%)
0.143
0.2%
0.16666666671
 
< 0.1%
0.252
0.3%
0.252
 
< 0.1%
0.329
0.1%
0.321
 
< 0.1%
0.354
 
< 0.1%
0.36666666671
 
< 0.1%
0.41
 
< 0.1%
0.438
0.2%
ValueCountFrequency (%)
37.51
< 0.1%
351
< 0.1%
301
< 0.1%
25.951
< 0.1%
22.21
< 0.1%
21.22
< 0.1%
211
< 0.1%
20.051
< 0.1%
19.81
< 0.1%
19.651
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct4538
Distinct (%)22.8%
Missing407
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean132.6371371
Minimum19
Maximum693.1333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:39.910830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile88
Q1109.625
median125
Q3146
95-th percentile202.9
Maximum693.1333333
Range674.1333333
Interquartile range (IQR)36.375

Descriptive statistics

Standard deviation39.28551348
Coefficient of variation (CV)0.2961878878
Kurtosis15.36827196
Mean132.6371371
Median Absolute Deviation (MAD)17.66666667
Skewness2.596670717
Sum2643325.505
Variance1543.351569
MonotonicityNot monotonic
2021-11-29T11:21:40.008960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105135
 
0.7%
112125
 
0.6%
120121
 
0.6%
121118
 
0.6%
108118
 
0.6%
124118
 
0.6%
122117
 
0.6%
119117
 
0.6%
114116
 
0.6%
110113
 
0.6%
Other values (4528)18731
92.1%
(Missing)407
 
2.0%
ValueCountFrequency (%)
191
 
< 0.1%
311
 
< 0.1%
382
< 0.1%
401
 
< 0.1%
411
 
< 0.1%
422
< 0.1%
461
 
< 0.1%
472
< 0.1%
481
 
< 0.1%
513
< 0.1%
ValueCountFrequency (%)
693.13333331
< 0.1%
671.51
< 0.1%
6661
< 0.1%
5631
< 0.1%
521.33333331
< 0.1%
509.251
< 0.1%
5011
< 0.1%
499.33333331
< 0.1%
4721
< 0.1%
4631
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct1364
Distinct (%)17.6%
Missing12603
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean2.093051854
Minimum0.3
Maximum26.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:40.111684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11.2
median1.666666667
Q32.4
95-th percentile4.614
Maximum26.95
Range26.65
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.703575579
Coefficient of variation (CV)0.8139194333
Kurtosis35.54188518
Mean2.093051854
Median Absolute Deviation (MAD)0.5333333333
Skewness4.752367759
Sum16185.56998
Variance2.902169752
MonotonicityNot monotonic
2021-11-29T11:21:40.210922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1297
 
1.5%
1.2290
 
1.4%
1.3282
 
1.4%
1.4258
 
1.3%
0.9249
 
1.2%
1.1242
 
1.2%
1.5240
 
1.2%
1.6222
 
1.1%
1.7189
 
0.9%
1.8184
 
0.9%
Other values (1354)5280
26.0%
(Missing)12603
62.0%
ValueCountFrequency (%)
0.32
 
< 0.1%
0.3251
 
< 0.1%
0.371
 
< 0.1%
0.44
 
< 0.1%
0.519
 
0.1%
0.557
 
< 0.1%
0.56666666672
 
< 0.1%
0.5751
 
< 0.1%
0.652
0.3%
0.63
 
< 0.1%
ValueCountFrequency (%)
26.951
< 0.1%
25.051
< 0.1%
21.788888891
< 0.1%
191
< 0.1%
18.72
< 0.1%
18.6251
< 0.1%
17.81
< 0.1%
17.51
< 0.1%
17.1251
< 0.1%
16.806666671
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct739
Distinct (%)3.9%
Missing1388
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean2.018570403
Minimum0.8
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:40.309089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.6
Q11.8
median2
Q32.175
95-th percentile2.55
Maximum8.2
Range7.4
Interquartile range (IQR)0.375

Descriptive statistics

Standard deviation0.3182137285
Coefficient of variation (CV)0.1576431161
Kurtosis19.34391377
Mean2.018570403
Median Absolute Deviation (MAD)0.18
Skewness1.922416785
Sum38247.872
Variance0.101259977
MonotonicityNot monotonic
2021-11-29T11:21:40.409059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21491
 
7.3%
1.91247
 
6.1%
2.11147
 
5.6%
1.81081
 
5.3%
2.2797
 
3.9%
1.7773
 
3.8%
2.3531
 
2.6%
1.85480
 
2.4%
1.6475
 
2.3%
2.05472
 
2.3%
Other values (729)10454
51.4%
(Missing)1388
 
6.8%
ValueCountFrequency (%)
0.81
 
< 0.1%
0.92
 
< 0.1%
18
 
< 0.1%
1.052
 
< 0.1%
1.0666666671
 
< 0.1%
1.111
 
0.1%
1.151
 
< 0.1%
1.1666666671
 
< 0.1%
1.228
0.1%
1.22
 
< 0.1%
ValueCountFrequency (%)
8.21
< 0.1%
6.81
< 0.1%
6.6751
< 0.1%
6.51
< 0.1%
6.21
< 0.1%
4.7751
< 0.1%
4.51
< 0.1%
4.22
< 0.1%
4.121
< 0.1%
4.12
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1262
Distinct (%)7.6%
Missing3650
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean3.557916442
Minimum0.45
Maximum14.46666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:40.514620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile2.1
Q12.8
median3.35
Q34.05
95-th percentile5.7
Maximum14.46666667
Range14.01666667
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.19359207
Coefficient of variation (CV)0.3354750144
Kurtosis6.687354431
Mean3.557916442
Median Absolute Deviation (MAD)0.6
Skewness1.789741422
Sum59367.39375
Variance1.424662029
MonotonicityNot monotonic
2021-11-29T11:21:40.610141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5443
 
2.2%
3432
 
2.1%
3.1425
 
2.1%
3.3417
 
2.1%
3.2411
 
2.0%
3.4401
 
2.0%
2.8370
 
1.8%
3.6362
 
1.8%
2.9362
 
1.8%
3.7355
 
1.7%
Other values (1252)12708
62.5%
(Missing)3650
 
17.9%
ValueCountFrequency (%)
0.451
 
< 0.1%
0.71
 
< 0.1%
0.82
 
< 0.1%
0.93
< 0.1%
0.91428571431
 
< 0.1%
0.952
 
< 0.1%
0.9751
 
< 0.1%
15
< 0.1%
1.061
 
< 0.1%
1.11
 
< 0.1%
ValueCountFrequency (%)
14.466666671
< 0.1%
14.451
< 0.1%
13.3751
< 0.1%
13.31
< 0.1%
13.216666671
< 0.1%
131
< 0.1%
12.91
< 0.1%
12.41
< 0.1%
12.11
< 0.1%
11.966666671
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct1565
Distinct (%)7.9%
Missing433
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.135700082
Minimum2.2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:40.712688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.45
Q13.806904762
median4.1
Q34.4
95-th percentile5
Maximum9
Range6.8
Interquartile range (IQR)0.5930952381

Descriptive statistics

Standard deviation0.4834578783
Coefficient of variation (CV)0.1168986795
Kurtosis2.579263651
Mean4.135700082
Median Absolute Deviation (MAD)0.3
Skewness0.8839187897
Sum82312.83873
Variance0.2337315201
MonotonicityNot monotonic
2021-11-29T11:21:40.810579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4839
 
4.1%
3.9723
 
3.6%
4.1616
 
3.0%
4.2614
 
3.0%
3.8555
 
2.7%
4.3533
 
2.6%
3.7441
 
2.2%
4.5404
 
2.0%
4.4403
 
2.0%
3.6388
 
1.9%
Other values (1555)14387
70.7%
(Missing)433
 
2.1%
ValueCountFrequency (%)
2.21
 
< 0.1%
2.51
 
< 0.1%
2.651
 
< 0.1%
2.6666666671
 
< 0.1%
2.73
< 0.1%
2.721
 
< 0.1%
2.753
< 0.1%
2.87
< 0.1%
2.821
 
< 0.1%
2.851
 
< 0.1%
ValueCountFrequency (%)
91
< 0.1%
8.461
< 0.1%
7.11
< 0.1%
6.981
< 0.1%
6.91
< 0.1%
6.851
< 0.1%
6.81
< 0.1%
6.81
< 0.1%
6.7751
< 0.1%
6.751
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct662
Distinct (%)11.5%
Missing14566
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean1.760810069
Minimum0.1
Maximum45.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:40.912201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.366666667
95-th percentile6.571
Maximum45.9
Range45.8
Interquartile range (IQR)0.9666666667

Descriptive statistics

Standard deviation3.840258073
Coefficient of variation (CV)2.180960992
Kurtosis44.17062061
Mean1.760810069
Median Absolute Deviation (MAD)0.35
Skewness5.964460236
Sum10159.8741
Variance14.74758207
MonotonicityNot monotonic
2021-11-29T11:21:41.011735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3496
 
2.4%
0.5487
 
2.4%
0.4477
 
2.3%
0.6390
 
1.9%
0.7352
 
1.7%
0.2308
 
1.5%
0.8286
 
1.4%
0.9224
 
1.1%
1175
 
0.9%
1.1122
 
0.6%
Other values (652)2453
 
12.1%
(Missing)14566
71.6%
ValueCountFrequency (%)
0.159
 
0.3%
0.1251
 
< 0.1%
0.13333333334
 
< 0.1%
0.1511
 
0.1%
0.16666666672
 
< 0.1%
0.21
 
< 0.1%
0.2308
1.5%
0.22
 
< 0.1%
0.221
 
< 0.1%
0.2251
 
< 0.1%
ValueCountFrequency (%)
45.91
< 0.1%
45.751
< 0.1%
44.966666671
< 0.1%
44.11
< 0.1%
43.21
< 0.1%
42.351
< 0.1%
40.751
< 0.1%
40.151
< 0.1%
40.033333331
< 0.1%
36.31
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct305
Distinct (%)62.4%
Missing19847
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean8.906701104
Minimum0.3
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:41.186710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3566666667
Q10.8
median3.55
Q312.5
95-th percentile35.84
Maximum48
Range47.7
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation11.37407288
Coefficient of variation (CV)1.277024203
Kurtosis1.640371742
Mean8.906701104
Median Absolute Deviation (MAD)3.05
Skewness1.58091301
Sum4355.37684
Variance129.3695339
MonotonicityNot monotonic
2021-11-29T11:21:41.289908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.524
 
0.1%
0.322
 
0.1%
0.420
 
0.1%
0.817
 
0.1%
0.612
 
0.1%
0.711
 
0.1%
19
 
< 0.1%
1.16
 
< 0.1%
1.156
 
< 0.1%
0.95
 
< 0.1%
Other values (295)357
 
1.8%
(Missing)19847
97.6%
ValueCountFrequency (%)
0.322
0.1%
0.353
 
< 0.1%
0.36666666671
 
< 0.1%
0.420
0.1%
0.4251
 
< 0.1%
0.451
 
< 0.1%
0.452
 
< 0.1%
0.46666666672
 
< 0.1%
0.524
0.1%
0.53333333332
 
< 0.1%
ValueCountFrequency (%)
481
< 0.1%
46.51
< 0.1%
461
< 0.1%
452
< 0.1%
44.81
< 0.1%
44.21
< 0.1%
42.91
< 0.1%
421
< 0.1%
41.51
< 0.1%
411
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5008
Distinct (%)25.1%
Missing364
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean31.63387892
Minimum10.25
Maximum68.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:41.390841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.25
5-th percentile25.20423077
Q128.35
median31
Q334.46666667
95-th percentile40.1
Maximum68.04
Range57.79
Interquartile range (IQR)6.116666667

Descriptive statistics

Standard deviation4.612103956
Coefficient of variation (CV)0.145796346
Kurtosis0.9954907795
Mean31.63387892
Median Absolute Deviation (MAD)2.966666667
Skewness0.6914464824
Sum631791.8298
Variance21.2715029
MonotonicityNot monotonic
2021-11-29T11:21:41.484396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.572
 
0.4%
31.572
 
0.4%
3571
 
0.3%
3071
 
0.3%
3169
 
0.3%
2968
 
0.3%
30.566
 
0.3%
32.165
 
0.3%
33.463
 
0.3%
32.562
 
0.3%
Other values (4998)19293
94.9%
(Missing)364
 
1.8%
ValueCountFrequency (%)
10.251
< 0.1%
11.861
< 0.1%
14.833333331
< 0.1%
16.11
< 0.1%
16.81
< 0.1%
17.466666671
< 0.1%
18.251
< 0.1%
18.366666671
< 0.1%
18.41
< 0.1%
18.51
< 0.1%
ValueCountFrequency (%)
68.041
< 0.1%
62.451
< 0.1%
61.71
< 0.1%
61.051
< 0.1%
581
< 0.1%
56.38751
< 0.1%
56.11
< 0.1%
55.851
< 0.1%
55.533333331
< 0.1%
54.751
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2355
Distinct (%)11.9%
Missing507
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean10.75987035
Minimum3.325
Maximum20.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:41.585872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.325
5-th percentile8.4
Q19.6
median10.55
Q311.75
95-th percentile13.8
Maximum20.96
Range17.635
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.637669715
Coefficient of variation (CV)0.1522016215
Kurtosis0.5062771099
Mean10.75987035
Median Absolute Deviation (MAD)1.05
Skewness0.5812765727
Sum213357.4692
Variance2.681962094
MonotonicityNot monotonic
2021-11-29T11:21:41.687922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5247
 
1.2%
10225
 
1.1%
10.6204
 
1.0%
9.6199
 
1.0%
10.9199
 
1.0%
10.3196
 
1.0%
9.5195
 
1.0%
9.8194
 
1.0%
9.9193
 
0.9%
11.1191
 
0.9%
Other values (2345)17786
87.5%
(Missing)507
 
2.5%
ValueCountFrequency (%)
3.3251
< 0.1%
4.1751
< 0.1%
5.42
< 0.1%
5.61
< 0.1%
5.91
< 0.1%
61
< 0.1%
6.0251
< 0.1%
6.051
< 0.1%
6.151
< 0.1%
6.21
< 0.1%
ValueCountFrequency (%)
20.961
< 0.1%
19.81
< 0.1%
19.32
< 0.1%
18.81
< 0.1%
18.61
< 0.1%
18.451
< 0.1%
18.051
< 0.1%
17.951
< 0.1%
17.61
< 0.1%
17.51
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct4027
Distinct (%)25.4%
Missing4496
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean36.56864135
Minimum17.1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:41.794284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile23.1
Q127.1
median31.15
Q338.6
95-th percentile72.55166667
Maximum150
Range132.9
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation16.72308365
Coefficient of variation (CV)0.4573066714
Kurtosis9.733848023
Mean36.56864135
Median Absolute Deviation (MAD)5
Skewness2.74209416
Sum579247.279
Variance279.6615269
MonotonicityNot monotonic
2021-11-29T11:21:41.897088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.583
 
0.4%
27.683
 
0.4%
2779
 
0.4%
2878
 
0.4%
29.377
 
0.4%
28.176
 
0.4%
27.775
 
0.4%
28.674
 
0.4%
29.573
 
0.4%
28.372
 
0.4%
Other values (4017)15070
74.1%
(Missing)4496
 
22.1%
ValueCountFrequency (%)
17.11
< 0.1%
17.21
< 0.1%
17.31
< 0.1%
17.651
< 0.1%
18.11
< 0.1%
18.251
< 0.1%
18.42
< 0.1%
18.451
< 0.1%
18.52
< 0.1%
18.62
< 0.1%
ValueCountFrequency (%)
15034
0.2%
145.91
 
< 0.1%
145.451
 
< 0.1%
143.71
 
< 0.1%
142.11
 
< 0.1%
140.61
 
< 0.1%
1391
 
< 0.1%
137.81
 
< 0.1%
137.31
 
< 0.1%
137.151
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct3287
Distinct (%)16.7%
Missing625
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean11.76131034
Minimum0.1
Maximum305.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:41.998784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.05
Q18.25
median10.875
Q314
95-th percentile20.6
Maximum305.75
Range305.65
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation6.61279845
Coefficient of variation (CV)0.5622501453
Kurtosis302.681305
Mean11.76131034
Median Absolute Deviation (MAD)2.85
Skewness10.36808671
Sum231827.1882
Variance43.72910335
MonotonicityNot monotonic
2021-11-29T11:21:42.094107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.9107
 
0.5%
10106
 
0.5%
9.5106
 
0.5%
10.2101
 
0.5%
10.3101
 
0.5%
9100
 
0.5%
8.6100
 
0.5%
899
 
0.5%
8.498
 
0.5%
9.197
 
0.5%
Other values (3277)18696
91.9%
(Missing)625
 
3.1%
ValueCountFrequency (%)
0.14
< 0.1%
0.12
 
< 0.1%
0.151
 
< 0.1%
0.151
 
< 0.1%
0.16666666671
 
< 0.1%
0.28
< 0.1%
0.21
 
< 0.1%
0.23333333331
 
< 0.1%
0.251
 
< 0.1%
0.32
 
< 0.1%
ValueCountFrequency (%)
305.751
< 0.1%
203.16666671
< 0.1%
168.61
< 0.1%
151.76666671
< 0.1%
147.17142861
< 0.1%
134.2251
< 0.1%
126.21
< 0.1%
120.51
< 0.1%
119.91
< 0.1%
116.51
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct1108
Distinct (%)43.2%
Missing17769
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean311.7025739
Minimum52.5
Maximum1383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:42.193664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum52.5
5-th percentile125.8833333
Q1193.3166667
median264
Q3386.5
95-th percentile654.85
Maximum1383
Range1330.5
Interquartile range (IQR)193.1833333

Descriptive statistics

Standard deviation167.4443938
Coefficient of variation (CV)0.5371928493
Kurtosis2.662096449
Mean311.7025739
Median Absolute Deviation (MAD)85
Skewness1.44842109
Sum800140.5073
Variance28037.62502
MonotonicityNot monotonic
2021-11-29T11:21:42.297056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21414
 
0.1%
22212
 
0.1%
15112
 
0.1%
23912
 
0.1%
18512
 
0.1%
21712
 
0.1%
20211
 
0.1%
16311
 
0.1%
21611
 
0.1%
24811
 
0.1%
Other values (1098)2449
 
12.0%
(Missing)17769
87.4%
ValueCountFrequency (%)
52.51
< 0.1%
581
< 0.1%
631
< 0.1%
64.916666671
< 0.1%
651
< 0.1%
75.3751
< 0.1%
75.751
< 0.1%
762
< 0.1%
79.666666671
< 0.1%
80.751
< 0.1%
ValueCountFrequency (%)
13831
< 0.1%
12461
< 0.1%
12111
< 0.1%
11611
< 0.1%
10301
< 0.1%
10001
< 0.1%
9761
< 0.1%
9601
< 0.1%
9561
< 0.1%
9461
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct3464
Distinct (%)17.5%
Missing585
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean212.0161986
Minimum9.333333333
Maximum1687.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:42.400754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.333333333
5-th percentile85.25
Q1144.3333333
median193
Q3256
95-th percentile400.5
Maximum1687.5
Range1678.166667
Interquartile range (IQR)111.6666667

Descriptive statistics

Standard deviation105.2199416
Coefficient of variation (CV)0.4962825589
Kurtosis10.57167137
Mean212.0161986
Median Absolute Deviation (MAD)54.6
Skewness2.070866857
Sum4187531.939
Variance11071.23611
MonotonicityNot monotonic
2021-11-29T11:21:42.499841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18469
 
0.3%
18069
 
0.3%
16768
 
0.3%
20168
 
0.3%
19567
 
0.3%
18564
 
0.3%
21164
 
0.3%
16063
 
0.3%
16363
 
0.3%
18962
 
0.3%
Other values (3454)19094
93.9%
(Missing)585
 
2.9%
ValueCountFrequency (%)
9.3333333331
< 0.1%
9.51
< 0.1%
9.7272727271
< 0.1%
10.21
< 0.1%
12.8751
< 0.1%
132
< 0.1%
142
< 0.1%
15.666666671
< 0.1%
171
< 0.1%
17.333333332
< 0.1%
ValueCountFrequency (%)
1687.51
< 0.1%
15441
< 0.1%
13431
< 0.1%
1300.51
< 0.1%
1187.751
< 0.1%
1113.751
< 0.1%
1101.3333331
< 0.1%
1096.51
< 0.1%
1067.51
< 0.1%
1056.51
< 0.1%

Age
Real number (ℝ≥0)

Distinct7280
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.6216129
Minimum18.11
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:42.681428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.11
5-th percentile30.8475
Q152.29
median64.67
Q375.66
95-th percentile84.95
Maximum89
Range70.89
Interquartile range (IQR)23.37

Descriptive statistics

Standard deviation16.23615352
Coefficient of variation (CV)0.2592739594
Kurtosis-0.2520549698
Mean62.6216129
Median Absolute Deviation (MAD)11.61
Skewness-0.5902903968
Sum1273473.12
Variance263.6126812
MonotonicityNot monotonic
2021-11-29T11:21:42.783772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.8213
 
0.1%
68.1712
 
0.1%
71.3712
 
0.1%
65.4712
 
0.1%
69.6812
 
0.1%
72.8111
 
0.1%
69.5811
 
0.1%
64.8611
 
0.1%
62.511
 
0.1%
77.7711
 
0.1%
Other values (7270)20220
99.4%
ValueCountFrequency (%)
18.113
< 0.1%
18.131
 
< 0.1%
18.142
< 0.1%
18.151
 
< 0.1%
18.181
 
< 0.1%
18.241
 
< 0.1%
18.321
 
< 0.1%
18.341
 
< 0.1%
18.352
< 0.1%
18.361
 
< 0.1%
ValueCountFrequency (%)
891
 
< 0.1%
88.991
 
< 0.1%
88.982
< 0.1%
88.974
< 0.1%
88.961
 
< 0.1%
88.951
 
< 0.1%
88.953
< 0.1%
88.942
< 0.1%
88.931
 
< 0.1%
88.921
 
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
1.0
11834 
0.0
8502 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61008
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.011834
58.2%
0.08502
41.8%

Length

2021-11-29T11:21:42.883882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:21:42.938019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.011834
58.2%
0.08502
41.8%

Most occurring characters

ValueCountFrequency (%)
028838
47.3%
.20336
33.3%
111834
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40672
66.7%
Other Punctuation20336
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028838
70.9%
111834
29.1%
Other Punctuation
ValueCountFrequency (%)
.20336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common61008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028838
47.3%
.20336
33.3%
111834
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII61008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028838
47.3%
.20336
33.3%
111834
19.4%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
0.0
5470 
1.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05470
26.9%
1.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:21:42.991216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:21:43.041044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.05470
50.6%
1.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016284
75.3%
15344
 
24.7%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
1.0
5470 
0.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05470
26.9%
0.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:21:43.094191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:21:43.144142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.05470
50.6%
0.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016158
74.7%
15470
 
25.3%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

HospAdmTime
Real number (ℝ)

Distinct7676
Distinct (%)37.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-48.67841062
Minimum-3710.66
Maximum23.99
Zeros168
Zeros (%)0.8%
Negative19912
Negative (%)97.9%
Memory size159.0 KiB
2021-11-29T11:21:43.208997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3710.66
5-th percentile-236.12
Q1-34.135
median-2.77
Q3-0.02
95-th percentile-0.01
Maximum23.99
Range3734.65
Interquartile range (IQR)34.115

Descriptive statistics

Standard deviation143.6833182
Coefficient of variation (CV)-2.951684666
Kurtosis123.0885825
Mean-48.67841062
Median Absolute Deviation (MAD)2.75
Skewness-8.542504055
Sum-989875.48
Variance20644.89593
MonotonicityNot monotonic
2021-11-29T11:21:43.311710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.023561
 
17.5%
-0.031635
 
8.0%
-0.011065
 
5.2%
-0.04631
 
3.1%
-0.03339
 
1.7%
-0.03316
 
1.6%
-0.05256
 
1.3%
0168
 
0.8%
-0.02122
 
0.6%
-0.0691
 
0.4%
Other values (7666)12151
59.8%
ValueCountFrequency (%)
-3710.661
< 0.1%
-3322.91
< 0.1%
-3269.11
< 0.1%
-3212.561
< 0.1%
-3141.551
< 0.1%
-2668.771
< 0.1%
-2562.531
< 0.1%
-2506.691
< 0.1%
-2476.581
< 0.1%
-2379.761
< 0.1%
ValueCountFrequency (%)
23.991
< 0.1%
22.041
< 0.1%
20.041
< 0.1%
17.341
< 0.1%
16.021
< 0.1%
14.651
< 0.1%
14.211
< 0.1%
141
< 0.1%
11.941
< 0.1%
10.991
< 0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct226
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.84522522
Minimum4.5
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:43.421060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile8.5
Q114
median20.5
Q325
95-th percentile30
Maximum320
Range315.5
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.71782428
Coefficient of variation (CV)0.5621346929
Kurtosis84.39630215
Mean20.84522522
Median Absolute Deviation (MAD)5.5
Skewness6.167088074
Sum423908.5
Variance137.3074058
MonotonicityNot monotonic
2021-11-29T11:21:43.517468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.5626
 
3.1%
22619
 
3.0%
21598
 
2.9%
23.5597
 
2.9%
20.5596
 
2.9%
20591
 
2.9%
23585
 
2.9%
22.5579
 
2.8%
24570
 
2.8%
19.5570
 
2.8%
Other values (216)14405
70.8%
ValueCountFrequency (%)
4.588
 
0.4%
587
 
0.4%
5.574
 
0.4%
687
 
0.4%
6.591
 
0.4%
7110
0.5%
7.5149
0.7%
8157
0.8%
8.5206
1.0%
9261
1.3%
ValueCountFrequency (%)
3201
 
< 0.1%
3091
 
< 0.1%
2861
 
< 0.1%
169.51
 
< 0.1%
1691
 
< 0.1%
168.55
< 0.1%
165.51
 
< 0.1%
164.51
 
< 0.1%
1541
 
< 0.1%
1531
 
< 0.1%

SepsisLabel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct350
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03360332271
Minimum0
Maximum1
Zeros18546
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:43.622316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2083333333
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1431976379
Coefficient of variation (CV)4.261413049
Kurtosis27.7509207
Mean0.03360332271
Median Absolute Deviation (MAD)0
Skewness5.164975623
Sum683.3571706
Variance0.02050556349
MonotonicityNot monotonic
2021-11-29T11:21:43.730390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018546
91.2%
1203
 
1.0%
0.833333333333
 
0.2%
0.588235294131
 
0.2%
0.909090909130
 
0.1%
0.666666666727
 
0.1%
0.476190476225
 
0.1%
0.714285714324
 
0.1%
0.555555555623
 
0.1%
0.523
 
0.1%
Other values (340)1371
 
6.7%
ValueCountFrequency (%)
018546
91.2%
0.017857142861
 
< 0.1%
0.027272727271
 
< 0.1%
0.029508196721
 
< 0.1%
0.030487804881
 
< 0.1%
0.031468531471
 
< 0.1%
0.032490974731
 
< 0.1%
0.032727272731
 
< 0.1%
0.032786885251
 
< 0.1%
0.033582089551
 
< 0.1%
ValueCountFrequency (%)
1203
1.0%
0.909090909130
 
0.1%
0.917
 
0.1%
0.833333333333
 
0.2%
0.818181818213
 
0.1%
0.769230769217
 
0.1%
0.7514
 
0.1%
0.714285714324
 
0.1%
0.692307692313
 
0.1%
0.666666666727
 
0.1%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
0.0
18546 
1.0
 
1790

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61008
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018546
91.2%
1.01790
 
8.8%

Length

2021-11-29T11:21:43.828058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:21:43.881923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.018546
91.2%
1.01790
 
8.8%

Most occurring characters

ValueCountFrequency (%)
038882
63.7%
.20336
33.3%
11790
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40672
66.7%
Other Punctuation20336
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
038882
95.6%
11790
 
4.4%
Other Punctuation
ValueCountFrequency (%)
.20336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common61008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
038882
63.7%
.20336
33.3%
11790
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII61008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
038882
63.7%
.20336
33.3%
11790
 
2.9%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.85793666
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:21:43.945377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q125
median39
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.30865867
Coefficient of variation (CV)0.5741081639
Kurtosis40.49091179
Mean38.85793666
Median Absolute Deviation (MAD)11
Skewness4.703211094
Sum790215
Variance497.6762517
MonotonicityNot monotonic
2021-11-29T11:21:44.120675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36700
 
3.4%
39665
 
3.3%
38656
 
3.2%
40634
 
3.1%
41632
 
3.1%
37632
 
3.1%
43600
 
3.0%
42597
 
2.9%
44580
 
2.9%
46560
 
2.8%
Other values (218)14080
69.2%
ValueCountFrequency (%)
8124
 
0.6%
9122
 
0.6%
1095
 
0.5%
11114
 
0.6%
12121
 
0.6%
13145
0.7%
14194
1.0%
15231
1.1%
16274
1.3%
17348
1.7%
ValueCountFrequency (%)
3365
< 0.1%
3351
 
< 0.1%
3341
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3052
 
< 0.1%
2971
 
< 0.1%
2861
 
< 0.1%
2791
 
< 0.1%
2771
 
< 0.1%

Interactions

2021-11-29T11:21:32.308262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.311266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.486921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.717038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.899812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.070552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.248819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.426451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.598729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.774528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.945094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.113065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.281887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.454435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.696074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.874070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.042549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.211087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.398396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.574780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.758889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.930899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.123400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.305031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.475966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.655176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.910443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.088915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.258087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.420817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.596868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.782292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.960885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.137520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.322865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.509362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.690190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.953553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:32.130978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:32.397969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.397913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.628715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.807224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:25.983269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.157635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.335892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.510415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.685232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:26.857703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.027930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.195491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.366296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.536594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.783446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:27.955577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.125970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.302566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.484909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.665105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:28.844222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.031412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.213304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.388085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.563421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.818405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:29.998341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.172386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.335562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.507211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.687369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:30.870469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.048483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.226524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.414004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.597307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:31.783137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:32.040591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:21:32.217553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:21:44.270116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:21:44.619308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:21:44.966636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:21:45.255177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:21:32.644915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:21:33.740680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:21:34.464549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:21:35.220299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
01101.57142991.47727336.778000126.80952487.261905NaN24.820000NaN20.71428646.5000000.2825007.34714395.33333386.50000016.00000018.00000098.09.45000085.0000000.700000NaN163.000000NaN2.1000003.5000004.2000000.300000NaN36.70000012.350000NaN10.200000NaN327.50000083.140.0NaNNaN-0.0327.50.000000.054.0
1260.95454597.00000036.165000136.60000066.70454544.06666714.236842NaNNaN22.000000NaNNaNNaNNaNNaN100.000000NaN7.900000113.0000002.500000NaN78.000000NaN2.5000004.4000005.100000NaNNaN27.8000009.700000NaN11.000000NaN158.00000075.910.00.01.0-98.6012.00.000000.023.0
2379.61111195.43181837.609375140.03333381.04800054.39285725.633333NaN6.50000030.6666670.6428577.49666739.500000NaNNaN28.666667NaN11.00000099.0000000.866667NaN98.000000NaN2.4333332.5333333.775000NaNNaN28.6500009.63333330.0000009.000000NaN479.66666745.820.01.00.0-1195.7124.50.000000.048.0
34102.44444498.20370436.455000113.01923167.14730851.42857118.884615NaN0.00000022.000000NaN7.39000043.00000097.833333NaN16.500000NaN8.200000106.5000000.800000NaN135.333333NaN2.0500003.8000004.433333NaNNaN25.8000008.30000021.8000007.600000NaN182.00000065.710.00.01.0-8.7715.00.000000.029.0
4573.91666797.50000036.992222132.77083387.088235NaN16.500000NaNNaN25.666667NaNNaNNaNNaN20.6666677.33333369.08.166667105.3333330.633333NaN123.000000NaN2.2000002.8666673.5666670.566667NaN42.13333314.70000029.0000006.933333NaN279.00000028.091.01.00.0-0.0525.50.000000.048.0
56100.00000098.43750036.600000125.93333388.110667NaN26.583333NaN0.00000029.0000000.4000007.34000047.000000NaNNaN9.000000NaNNaN111.0000000.700000NaN144.6666671.400000NaNNaN3.800000NaNNaN36.90000012.200000NaN12.000000NaN298.00000052.011.01.00.0-0.0311.00.000000.017.0
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